Table 1.

Baseline characteristics of BG^a level-based studies (N=10).

First author (year), country	Data source	Sample size			Demographic information		Object; setting		Model; PHb (minutes); input		Performance metrics
		Patients, n	Data points, n
Pérez-Gandía (2010), Spain [20]	CGMc device	15	728	—d		T1DMe; out		Models: NNMf, ARMg PH: 15, 30 Input: CGM data		RMSEh, delay
Prendin (2021) United States [21]	CGM device	Real (n=141)	350,000	Age		T1DM; out		ARM, autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), SVMi, RFj feed-forward neural network (fNN), long short-term memory (LSTM) PH: 30 Input: CGM data		RMSE, coefficient of determination (COD) sensibility, delay, precision F1 score, time gain
Zhu (2020) England [22]	Ohio T1DM, UVA/Padova T1D	Real (n=6), simulated (n=10)	1,036,800	—		T1DM; out		DRNNk, NNM, SVM, ARM PH:30 Input: BG level, meals, exercise, meal times		RMSE, mean absolute relative difference (MARD) time gain
D'Antoni (2020), Italy [49]	Ohio T1DM	6	—	Age, sex ratio		T1DM; out		ARJNNl, RF, SVM, autoregression (AR), one symbolic model (SAX), recurrent neural network (RNN), one neural network model (NARX), jump neural network (JNN), delayed feed-forward neural network model (DFFNN) PH: 15, 30 Input: CGM data		RMSE
Amar (2020), Israel [50]	CGM device, insulin pump	141	1,592,506	Age, sex ratio, weight, BMI, duration of DM		T1DM; in		ARM, gradually connected neural network (GCN), fully connected (FC [neural network]), light gradient boosting machine (LCBM), RF PH: 30, 60 Input: CGM data		RMSE, Clarke error grid (CEG)
Li (2020), England [51]	UVA/Padova T1D	Simulated (n=10)	51,840	—		T1DM; out		GluNet, NNM, SVM, latent variable with exogenous input (LVX), ARM PH: 30, 60 Input: BG level, meals, exercise		RMSE, MARD, time lag
Zecchin (2012), Italy [52]	UVA/Padova T1D, CGM device	Simulated (n=20), real (n=15)	—	—		T1DM; out		Neural network–linear prediction algorithm (NN-LPA), NN, ARM PH: 30 Input: meals, insulin		RMSE, energy of second-order differences (ESOD), time gain, J index
Mohebbi (2020), Denmark [53]	Cornerstones4Care platform	Real (n=50	—	—		T1DM; in		LSTM, ARIMA PH: 15, 30, 45, 60, 90		RMSE, MAE
Daniels (2022), England [54]	CGM device	Real (n=12)	—	Sex ratio		T1DM; out		Convolutional recurrent neural network (CRNN), SVM PH: 30, 45, 60, 90, 120 Input: BG level, insulin, meals, exercise		RMSE, MAE, CEG, time gain
Alfian (2020), Korea [55]	CGM device	Real (n=12)	26,723	—		—		SVM, k-nearest neighbor k-nearest neighbor (kNN), DTm, RF, AdaBoost, XGBoostn, NNM PH: 15, 30 Input: CGM data		RMSE, glucose-specific root mean square error (gRMSE), R2 score, mean absolute percentage error (MAPE)

^aBG: blood glucose.

^bPH: prediction horizon.

^cCGM: continuous glucose monitoring.

^dNot applicable.

^eT1DM: type 1 diabetes mellitus.

^fNNM: neural network model.

^gARM: autoregression model.

^hRMSE: root mean square error.

ⁱSVM: support vector machine.

^jRF: random forest.

^kDRNN: dilated recurrent neural network.

^lARJNN: ARTiDe jump neural network.

^mDT: decision tree.

ⁿXGBoost: Extreme Gradient Boosting.